Background: Autoimmune diseases (ADs) present significant health challenges globally, especially among adolescents and young adults (AYAs) due to their unique developmental stages. Comprehensive analyses of their burden are limited. This study leverages the Global Burden of Disease (GBD) 2021 data to assess the global, regional, and national burden and trends of major ADs among AYAs from 1990 to 2021. Methods: Utilizing data from the Global Burden of Disease (GBD) Study 2021 for individuals aged 15–39 years, we employed a direct method for age standardization to calculate estimates along with 95% uncertainty intervals (UIs) for assessing the age-standardized incidence rates (ASIR), prevalence rates (ASPR), and mortality rates (ASMR) of ADs. The diseases analyzed included rheumatoid arthritis (RA), inflammatory bowel disease (IBD), multiple sclerosis (MS), type 1 diabetes mellitus (T1DM), Asthma, and Psoriasis. Trends from 1990 to 2021 were analyzed using Joinpoint regression, providing average annual percentage changes (AAPC) and 95% confidence intervals (CIs). Result: In 2021, the global ASIR, ASPR, and ASMR of RA among AYAs (per 100,000 population) were 9.46 (95% UI: 5.92 to 13.54), 104.35 (77.44 to 137.84), and 0.016 (0.013 to 0.019), respectively. For IBD, the corresponding rates were 4.08 (3.07 to 5.37), 29.55 (23.00 to 37.83), and 0.10 (0.07 to 0.12). MS exhibited rates of 1.40 (0.93 to 1.93), 16.05 (12.73 to 19.75), and 0.05 (0.04 to 0.05), respectively. T1DM had rates of 6.63 (3.08 to 11.84), 245.51 (194.21 to 307.56), and 0.54 (0.47 to 0.60). Asthma demonstrated rates of 232.22 (132.11 to 361.24), 2245.51 (1671.05 to 2917.57), and 0.89 (0.77 to 1.08). Psoriasis showed rates of 55.08 (48.53 to 61.93) and 426.16 (394.12 to 460.18) for ASIR and ASPR, respectively. From 1990 to 2021, the global ASIR of RA (AAPC = 0.47, 95% CI: 0.46 to 0.49), IBD (0.22 [0.12 to 0.33]), MS (0.22 [0.19 to 0.26]), T1DM (0.83 [0.80 to 0.86]), and Psoriasis (0.33 [0.31 to 0.34]) showed increasing trends, whereas Asthma (-0.96 [-1.03 to -0.88]) showed a decreasing trend. The global ASPR of RA (0.70 [0.68 to 0.73]), MS (0.35 [0.32 to 0.37]), T1DM (0.68 [0.66 to 0.69]), and Psoriasis (0.29 [0.27 to 0.32]) also showed increasing trends, whereas IBD (-0.20 [-0.27 to -0.13]) and Asthma (-1.25 [-1.31 to -1.19]) showed decreasing trends. Notably, the estimated global ASMR of RA (-2.35 [-2.57 to -2.12]), MS (-0.63 [-0.86 to -0.41]), T1DM (-0.35 [-0.56 to -0.14]), and Asthma (-1.35 [-1.44 to -1.26]) in AYAs declined. Additionally, the burden of disease for ADs in AYAs varies considerably across continents and between 204 countries and territories. Conclusion: ADs among AYAs present a substantial public health burden with notable regional disparities in incidence, prevalence, and mortality rates. Understanding these patterns is essential for developing targeted public health interventions and policies to mitigate the impact of ADs in this population.

Public-private partnerships (PPPs) have become increasingly important in improving the sustainability of society in China, with transportation being the largest investment area. However, the Social Risk Factors (SRFs) of transportation PPPs in China, which serve as a useful tool for distinguishing strengths and weaknesses for effective social risk management (SRM), have not been clearly identified. A conceptual model including 3 risk dimensions and 15 SRFs was proposed to mitigate social risks and improve the social sustainability of transportation PPP projects. A questionnaire survey conducted to investigate stakeholders’ opinions on the proposed SRFs demonstrated that all the SRFs were important. The SRFs can be used to evaluate social risks from economic, environmental, and social dimensions. Confirmatory factor analysis (CFA) verified the classification of the SRFs and indicated that all the risk dimensions contributed to social risks. The social and environmental impacts on social sustainability may contribute more to the generation of social risks. Furthermore, the concept of people-first PPPs was proposed to reduce social risks from the perspective of different stakeholders, with the interactions among different stakeholders being prioritized. The identified SRFs and their relationships can improve our understanding of SRM in the delivery of social sustainability and improve social resilience.

Background: HIV and sexually transmitted infections (STIs) are major global public health concerns. Over 1 million curable STIs occur every day among people aged 15 years to 49 years worldwide. Insufficient testing or screening substantially impedes the elimination of HIV and STI transmission. Objective: The aim of our study was to develop an HIV and STI risk prediction tool using machine learning algorithms. Methods: We used clinic consultations that tested for HIV and STIs at the Melbourne Sexual Health Centre between March 2, 2015, and December 31, 2018, as the development data set (training and testing data set). We also used 2 external validation data sets, including data from 2019 as external “validation data 1” and data from January 2020 and January 2021 as external “validation data 2.” We developed 34 machine learning models to assess the risk of acquiring HIV, syphilis, gonorrhea, and chlamydia. We created an online tool to generate an individual’s risk of HIV or an STI. Results: The important predictors for HIV and STI risk were gender, age, men who reported having sex with men, number of casual sexual partners, and condom use. Our machine learning–based risk prediction tool, named MySTIRisk, performed at an acceptable or excellent level on testing data sets (area under the curve [AUC] for HIV=0.78; AUC for syphilis=0.84; AUC for gonorrhea=0.78; AUC for chlamydia=0.70) and had stable performance on both external validation data from 2019 (AUC for HIV=0.79; AUC for syphilis=0.85; AUC for gonorrhea=0.81; AUC for chlamydia=0.69) and data from 2020-2021 (AUC for HIV=0.71; AUC for syphilis=0.84; AUC for gonorrhea=0.79; AUC for chlamydia=0.69). Conclusions: Our web-based risk prediction tool could accurately predict the risk of HIV and STIs for clinic attendees using simple self-reported questions. MySTIRisk could serve as an HIV and STI screening tool on clinic websites or digital health platforms to encourage individuals at risk of HIV or an STI to be tested or start HIV pre-exposure prophylaxis. The public can use this tool to assess their risk and then decide if they would attend a clinic for testing. Clinicians or public health workers can use this tool to identify high-risk individuals for further interventions.

Introduction: Cancer is a major public health problem and a global leading cause of death where the screening, diagnosis, prediction, survival estimation, and treatment of cancer and control measures are still a major challenge. The rise of Artificial Intelligence (AI) and Machine Learning (ML) techniques and their applications in various fields have brought immense value in providing insights into advancement in support of cancer control. Methods: A systematic and thematic analysis was performed on the Scopus database to identify the top 100 cited articles in cancer research. Data were analyzed using RStudio and VOSviewer.Var1.6.6. Results: The top 100 articles in AI and ML in cancer received a 33 920 citation score with a range of 108 to 5758 times. Doi Kunio from the USA was the most cited author with total number of citations (TNC = 663). Out of 43 contributed countries, 30% of the top 100 cited articles originated from the USA, and 10% originated from China. Among the 57 peer-reviewed journals, the “Expert Systems with Application” published 8% of the total articles. The results were presented in highlight technological advancement through AI and ML via the widespread use of Artificial Neural Network (ANNs), Deep Learning or machine learning techniques, Mammography-based Model, Convolutional Neural Networks (SC-CNN), and text mining techniques in the prediction, diagnosis, and prevention of various types of cancers towards cancer control. Conclusions: This bibliometric study provides detailed overview of the most cited empirical evidence in AI and ML adoption in cancer research that could efficiently help in designing future research. The innovations guarantee greater speed by using AI and ML in the detection and control of cancer to improve patient experience.

Antibacterial overuse, misuse and resistance have become a major global threat. The Joint Commission International (JCI) accreditation standards include quality improvement and patient safety, which is exemplified by antimicrobial stewardship. There are currently few reports on interventions to improve the quality of outpatient antibacterial prescribing.